1. Field
Example embodiments relate to a method and apparatus for extracting feature points using hierarchical image segmentation and an image based localization method using the extracted feature points.
2. Description of the Related Art
In order to allow a mobile object to perform localization without information about an environment and to establish information about the environment, localization and mapping are simultaneously and organically performed. This is called Simultaneous Localization and Mapping (SLAM).
In general, a SLAM algorithm may be applied to an image based localization system to analyze image information so as to perform localization. Existing research into such image based localization suggests a random selection method and a uniform segmentation method as an image segmentation method performed in a preprocessing procedure for localization.
In the random selection method, in order to extract new feature points from an image, areas which do not overlap with registered feature points are randomly selected and feature points are extracted from the selected areas.
In the uniform segmentation method, a current image is segmented into areas having a constant size and new feature points are extracted only when registered feature points are not included in the areas.
However, in the random selection method, if many feature points are extracted from a specific area, the feature points may be non-uniformly extracted from the overall area. In the uniform segmentation method, the number of extracted feature points may not be larger than the number of segmented areas. If localization performance deteriorates due to an actual environment or if feature points having similar feature amounts are extracted from repeated image patterns, there is no corresponding portion. Thus, data association performance may deteriorate or localization may fail.